Image acquisition device, goods shelf, monitoring method and device for goods shelf, and image recognition method

ABSTRACT

An image acquisition device, a goods shelf, a monitoring device for a goods shelf, a monitoring method for a goods shelf and an image recognition method are disclosed. The image acquisition device includes at least one reflective part which is configured to form a virtual image of an object through reflection of the object; and a camera which is configured to take a picture of the object and the virtual image of the object, so as to reduce a photograph blind zone.

CROSS REFERENCE TO RELATED APPLICATIONS

The application claims priority to Chinese patent application No.201810754763.3, filed on Jul. 10, 2018, the entire disclosure of whichis incorporated herein by reference as part of the present application.

TECHNICAL FIELD

Embodiments of the present disclosure relate to an image acquisitiondevice, a goods shelf, a monitoring device for a goods shelf, amonitoring method for a goods shelf and an image recognition method.

BACKGROUND

Nowadays, self-service supermarkets make future retail modes receive anincreasing amount of attention. In new retail modes, automatic detectingof commodity shortage and commodity misplacement through performingimage recognition with respect to commodities on goods shelf is asolution with low cost.

SUMMARY

At least one embodiment of the present disclosure provides an imageacquisition device, which comprises: at least one reflective part whichis configured to form a virtual image of an object through reflection ofthe object; and a camera which is configured to take a picture of theobject and the virtual image of the object, so as to reduce a photographblind zone.

For example, in at least one example of the image acquisition device,the reflective part is at a side of the object away from the camera; andthe reflective part is a plane mirror.

For example, in at least one example of the image acquisition device,the plane mirror is perpendicular to a plane where the object is placed;or the angle between the plane mirror and the plane where the object isplaced is greater than 90 degrees or smaller than 90 degrees.

At least one embodiment of the present disclosure provides a goodsshelf, which comprises a goods cabinet and an image acquisition device.The image acquisition device comprises at least one reflective part anda camera; the at least one reflective part is configured to form avirtual image of an object through reflection of the object; and thecamera is configured to take a picture of the object and a virtual imageof the object, so as to reduce a photograph blind zone; the goodscabinet comprises a baseplate; the baseplate is divided into a pluralityof recognition regions, the plurality of recognition regions arerespectively configured to support a plurality of kinds of objects; thecamera of the image acquisition device is at a side of the plurality ofkinds of objects away from the baseplate; and the at least onereflective part of the image acquisition device is at at least one sideof the plurality of kinds of objects in a direction along which theplurality of recognition regions are arranged in parallel.

For example, in at least one example of the goods shelf, the pluralityof recognition regions are arranged in parallel along a first direction;the at least one reflective part of the image acquisition devicecomprises a first reflective part, and the first reflective part is atone of the at least one side of the plurality of kinds of objects in thefirst direction.

For example, in at least one example of the goods shelf, the pluralityof recognition regions is further arranged in parallel along a seconddirection which intersects the first direction; and the at least onereflective part of the image acquisition device further comprises asecond reflective part, and the second reflective part is at another oneof the at least one side of the plurality of kinds of objects in thesecond direction.

For example, in at least one example of the goods shelf, the goodscabinet further comprises a roof-plate and a side-plate; the side-plateis at at least one side of the plurality of recognition regions in thefirst direction; the plurality of recognition regions is arranged inparallel along a first direction; the camera of the image acquisitiondevice is on a surface of the roof-plate closer to the baseplate; andthe at least one reflective part of the image acquisition device is on asurface of the side-plate closer to the plurality of recognitionregions.

For example, in at least one example of the goods shelf, the camera isat a centerline, which extends along a second direction intersecting thefirst direction, of the roof-plate.

For example, in at least one example of the goods shelf, the goodscabinet of the goods shelf further comprises a back plate; the backplate is at a side of the plurality of recognition regions in a seconddirection intersecting the first direction; the plurality of recognitionregions is further arranged in parallel along the second direction; andthe at least one reflective part is further on a surface of the backplate closer to the plurality of recognition regions.

At least one embodiment of the present disclosure provides a monitoringmethod for a goods shelf, which comprises: obtaining a determinationresult regarding whether or not an image of objects on the goods shelfcomprises an occlusion area; obtaining the image of the objects and animage of a virtual image of the objects and performing image recognitionbased on the image of the objects and an image region, which iscorresponding to the occlusion area, of the image of the virtual imagein a case where the determination result is that the image of theobjects comprises the occlusion area; and obtaining the image of theobjects and performing image recognition based on the image of theobjects in the case where the determination result is that the image ofthe objects does not comprise the occlusion area.

At least one embodiment of the present disclosure provides a monitoringdevice for a goods shelf, which comprises: a processor and a memory.Computer program instructions that are suitable to be executed by theprocessor are stored in the memory; upon the processor running thecomputer program instructions, the monitoring device for the goods shelfperforms the monitoring method provided by any embodiment of the presentdisclosure.

For example, in at least one example of the monitoring device, upon theprocessor running the computer program instructions, the monitoringdevice for the goods shelf further performs a following methodcomprises: determining whether or not at least one of a group consistingof commodity shortage and commodity misplacement exists based on aresult of the image recognition; and uploading at least one of a groupconsisting of information related to the commodity shortage andinformation related to the commodity misplacement to a server in a casewhere it is determined that the at least one of the group consisting ofthe commodity shortage and the commodity misplacement exists

For example, in at least one example of the monitoring device,performing of the image recognition based on the image of the objectsand the image region, which is corresponding to the occlusion area, ofthe image of the virtual image comprises: replacing the occlusion areawith the image region, which is corresponding to the occlusion area, ofthe image of the virtual image, so as to obtain a processed image; andperforming the image recognition based on the processed image.

For example, in at least one example of the monitoring device, upon theprocessor running the computer program instructions, the monitoringdevice for the goods shelf further performs a following methodcomprises: obtaining the occlusion area of the image of the objects.

For example, in at least one example of the monitoring device, theocclusion area is obtained through comparing the image of the objectsand the image of the virtual image.

For example, in at least one example of the monitoring device, theocclusion area is an object number reduction area of the image of theobjects; and a number of objects in the object number reduction area issmaller than a number of virtual objects in an image region, which iscorresponding to the object number reduction area, of the image of thevirtual image.

For example, in at least one example of the monitoring device, theocclusion area of the image of the objects is obtained based on lengthsof the objects, a height of a camera of an image acquisition device ofthe goods shelf with respect to a plane where the objects are located,and a distance between adjacent objects; or the occlusion area comprisesa recognition region at very outside of the plurality of recognitionregions.

For example, in at least one example of the monitoring device, upon theprocessor running the computer program instructions, the monitoringdevice for the goods shelf further performs a following methodcomprises: obtaining the image of the objects and the image of thevirtual image based on a picture which is taken by a camera of an imageacquisition device of the goods shelf.

For example, in at least one example of the monitoring device, upon theprocessor running the computer program instructions, the monitoringdevice for the goods shelf further performs a following methodcomprises: dividing the image of the objects into a plurality of firstimage regions; dividing the image of the virtual image into a pluralityof second image regions. The plurality of first image regions arerespectively corresponding to a plurality of recognition regions of abaseplate of the goods shelf; and the plurality of second image regionsare respectively corresponding to the plurality of recognition regionsof the baseplate of the goods shelf.

At least one embodiment of the present disclosure provides an imagerecognition method based on the goods shelf provided by any oneembodiment of the present disclosure, the image recognition methodcomprises: obtaining an image, which serves as an initial image, of thebaseplate of the goods shelf that is empty; obtaining an image, whichserves as a recognition image and is obtained through taking a pictureof the objects on the baseplate and a virtual image of the objects afterthe objects are placed on the baseplate, and comparing the recognitionimage and the initial image, so as to obtain an image of the virtualimage and an image of the objects; and determining whether or not ablind zone exists according to the image of the virtual image and theimage of the objects, and replacing a recognition region, which iscorresponding to the blind zone, of the image of the objects with arecognition region, which is corresponding to the blind zone, of theimage of the virtual image in a case where it is determined that theblind zone exists.

For example, in at least one example of the image recognition method,determining of whether or not a blind zone exists comprises: performingimage recognition with respect to objects in the image of the objectsand virtual objects, which is corresponding to the objects in the imageof the objects, in the image of the virtual image, respectively;determining that the blind zone does not exist in a case where theobjects in the image of the objects are the same as the virtual objects,which is corresponding to the objects in the image of the objects, inthe image of the virtual image; and determining that the blind zoneexists in a case where the objects in the image of the objects are notsame as the virtual objects, which is corresponding to the objects inthe image of the objects, in the image of the virtual image

At least one embodiment of the present disclosure provides an imagerecognition method based on the goods shelf provided by any oneembodiment of the present disclosure, the image recognition methodcomprises: obtaining an image, which serves as an initial image, of thebaseplate of the goods shelf that is empty; obtaining an image, whichserves as a recognition image and is obtained through taking a pictureof the objects on the baseplate and a virtual image of the objects afterthe objects are placed on the baseplate, and comparing the recognitionimage and the initial image, so as to obtain an image of the virtualimage and an image of the objects; and replacing a recognition region,which is corresponding to a blind zone, of the image of the objects witha recognition region, which is corresponding to the blind zone, of theimage of the virtual image.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to clearly illustrate the technical solution of the embodimentsof the disclosure, the drawings of the embodiments will be brieflydescribed in the following; it is obvious that the described drawingsare only related to some embodiments of the disclosure and thus are notlimitative of the disclosure.

FIG. 1 illustrates a schematic diagram of an image acquisition deviceprovided by at least an embodiment of the present disclosure;

FIG. 2A illustrates a schematic diagram of a goods shelf provided by atleast an embodiment of the present disclosure;

FIG. 2B illustrates another schematic diagram of a goods shelf providedby at least an embodiment of the present disclosure;

FIG. 3 illustrates an exemplary flow chart of an image recognitionmethod based on a goods shelf provided by at least an embodiment of thepresent disclosure;

FIG. 4 illustrates an exemplary flow chart of step S103 provided by atleast an embodiment of the present disclosure;

FIG. 5 is an exemplary schematic diagram of calculating of a blind zoneprovided by at least an embodiment of the present disclosure;

FIG. 6 illustrates another exemplary flow chart of an image recognitionmethod based on a goods shelf provided by at least an embodiment of thepresent disclosure;

and

FIG. 7 illustrates an exemplary block diagram of a monitoring device fora goods shelf provided by at least an embodiment of the presentdisclosure.

DETAILED DESCRIPTION

In order to make objects, technical details and advantages of theembodiments of the disclosure apparent, the technical solutions of theembodiments will be described in a clearly and fully understandable wayin connection with the drawings related to the embodiments of thedisclosure. Apparently, the described embodiments are just a part butnot all of the embodiments of the disclosure. Based on the describedembodiments herein, those skilled in the art can obtain otherembodiment(s), without any inventive work, which should be within thescope of the disclosure.

Unless otherwise defined, all the technical and scientific terms usedherein have the same meanings as commonly understood by one of ordinaryskill in the art to which the present disclosure belongs. The terms“first,” “second,” etc., which are used in the description and theclaims of the present application for disclosure, are not intended toindicate any sequence, amount or importance, but distinguish variouscomponents. Also, the terms such as “a,” “an,” etc., are not intended tolimit the amount, but indicate the existence of at least one. The terms“comprise,” “comprising,” “include,” “including,” etc., are intended tospecify that the elements or the objects stated before these termsencompass the elements or the objects and equivalents thereof listedafter these terms, but do not preclude the other elements or objects.The phrases “connect”, “connected”, etc., are not intended to define aphysical connection or mechanical connection, but may include anelectrical connection, directly or indirectly. “On,” “under,” “right,”“left” and the like are only used to indicate relative positionrelationship, and when the position of the object which is described ischanged, the relative position relationship may be changed accordingly.

The inventors of the present disclosure have noted that, a blind zonemay present when using a camera to perform image acquisition withrespect to objects (commodities) on a goods shelf. For example, theblind zone is a region that cannot be presented in an image acquired bythe camera. For example, the blind zone may be formed at an edge of thegoods shelf because the commodity with a smaller height are sheltered orblocked by the commodity with a larger height, and the blind zone maycause adverse influence on the accuracy and/or precision in detectingthe commodity shortage and the commodity misplacement.

At least one embodiment of the present disclosure provides an imageacquisition device, a goods shelf, a monitoring device for a goodsshelf, a monitoring method for a goods shelf and an image recognitionmethod. The image acquisition device, the goods shelf, the monitoringdevice for the goods shelf, the monitoring method for the goods shelf,and the image recognition method can reduce a photograph blind zone.

At least one embodiment of the present disclosure provides an imageacquisition device, which comprises: at least one reflective part whichis configured to form a virtual image of an object through reflection ofthe object; and a camera which is configured to take a picture of theobject and the virtual image of the object, so as to reduce a photographblind zone.

FIG. 1 illustrates a schematic diagram of an image acquisition deviceprovided by at least an embodiment of the present disclosure, and theimage acquisition device may be provided on a goods shelf (the goodscabinet), so as to acquire an image of an object or objects (commodityor commodities) placed on the baseplate of the goods shelf (the goodscabinet). The objects (commodities) on the baseplate of the goods shelf(the goods cabinet) may comprise a plurality of kinds of objects (forexample, a first object and a second object as illustrated in FIG. 1),and the lengths of the objects in the height direction of the goodsshelf may respectively include a plurality of lengths.

As illustrated in FIG. 1, the image acquisition device comprises atleast one reflective part 12 and a camera 11. The reflective part 12 isconfigured to generate the virtual image of the objects throughreflection of the objects, and for example, the reflective part 12 is apart having a reflective structure such as a reflective surface; thecamera 11 is configured to take a picture of the real image and thevirtual image of the objects, so as to reduce or even eliminate aphotograph blind zone 13.

It should be noted that, in some example, the real image of the objectsrepresents the objects (real objects) that are placed on the goodsshelf, the image of the real image of the objects means the image of theobjects (the image of real objects); the virtual image of the object isformed through optical imaging of the reflective part 12 (that is,reflection of the reflective part 12); the image of the virtual imageand the image of the real image of the objects (that is, the image ofreal objects) can be obtained through taking a picture of the virtualimage of the objects and the objects by the camera 11 and dividing thepicture which is taken by the camera 11 into the image of the objectsand the image of the virtual image; the photograph blind zone 13represents a region, which cannot be presented in an image acquired bythe camera 11, of the goods shelf.

For example, the reflective part 12 is provided at a side of the object(the first object or the second object) away from the camera 11 in thehorizontal direction (for example, the length direction of the goodsshelf). For example, the image acquisition surface of the camera 11faces toward the reflective surface of the reflective part 12.

In some examples, the image acquisition device does not comprise thereflective part 12, a blind zone may present when the camera performsimage acquisition with respect to objects with different heights (thatis, lengths in the height direction) or irregular objects. Asillustrated in FIG. 1, in the case where the camera takes a picture ofthe first object and the second object and the height of the secondobject, which is provided at the side of the first object away from thecamera, is smaller than the height of the first object, the secondobject is sheltered by the first object and thus the second objectcannot be captured or only part of the second object is captured by thecamera, that is, the second object cannot be presented in the imagetaken by the camera, or only part of the second object can be presentedin the image taken by the camera, such that the blind zone 13 is formed.

In some examples of the present disclosure, by adopting the imageacquisition device equipped with both of the reflective part and thecamera, the image (or image region) of a sheltered portion (a shelteredregion) can be obtained through a virtual image formed by the reflectivepart. For example, the image (or image region) of the second object inthe blind zone 13 can be obtained through the virtual image, which isformed by the reflective part, of the second object. It should be notedthat, a plurality of reflective parts may be provided in the imageacquisition device according to specific implementations.

In some examples of the present disclosure, the reflective part 12 is aplane mirror. In the case where the reflective part is a plane mirror,the camera can capture the virtual image of the second object, and thevirtual image of the second object is formed by the reflection of thelight which is originated from the second object and incident on theplane mirror. Even though the images, acquired by the camera, of thevirtual image and the real image of the second object (the image of thevirtual image and the image of the second object) are different (or notexactly the same), the image recognition with respect to the objects isnot adversely affected. For example, by adopting image processingmethods such as deep learning (through artificial neural network, forexample), the objects can be recognized with any one of the images beingtaken at different positions with respect to the objects.

In some examples of the present disclosure, the plane mirror isperpendicular to the plane where the object is placed (for example, thebaseplate of the goods shelf). In this case, the virtual image formed bythe plane mirror indicates the features of the objects withoutdistortions, in this case, the recognition difficulty of the image ofthe virtual image (the image obtained through taking a picture of thevirtual image) can be, for example, decreased. In some examples of thepresent disclosure, the angle between the plane mirror and the planewhere the object is placed also may be greater than 90 degrees orsmaller than 90 degrees. In the case where the angle between the planemirror and the plane where the object is placed is greater than 90degrees or is smaller than 90 degrees, even though part of features ofthe objects may be lost, but adverse influence on the recognition isminor. Therefore, the technical solution that the plane mirror is notperpendicular to the plane where the object is placed may be adoptedaccording to the characteristics of the objects to be captured if thetechnical solution that the plane mirror is perpendicular to the planewhere the object is placed cannot be adopted.

It should be noted that, the reflective part 12 is not limited to be theplane mirror; according to actual requirements in specificimplementations, the reflective part 12 also may be a curved mirror or aspecial-shaped mirror. For example, the reflective part 12 may be aconvex mirror, so as to enlarge the image acquisition region of thecamera 11.

At least one embodiment of the present disclosure provides a goodsshelf, which comprises a goods cabinet and an image acquisition deviceprovided by any embodiment of the present disclosure. The goods cabinetcomprises a baseplate; the baseplate is divided into a plurality ofrecognition regions, the plurality of recognition regions arerespectively configured to support and contain (or accommodate) aplurality of kinds of objects; the camera of the image acquisitiondevice is at a side of the plurality of kinds of objects away from thebaseplate; and at least one reflective part of the image acquisitiondevice is at at least one side of the plurality of kinds of objects in adirection along which the plurality of recognition regions are arrangedin parallel.

FIG. 2A illustrates a schematic diagram of a goods shelf provided by atleast an embodiment of the present disclosure. FIG. 2B illustratesanother schematic diagram of a goods shelf provided by at least anembodiment of the present disclosure. The goods shelf comprises a goodscabinet (not labelled with a character numeral in related figures) andthe image acquisition device provided by any embodiment of the presentdisclosure.

As illustrated in FIG. 2A and FIG. 2B, the goods cabinet comprises aframe 101, a roof-plate 1, a side-plate 6, a back plate 102, and abaseplate 3, and the above-mentioned roof-plate 1, the side-plate 6, theback plate 102 and the baseplate 3 are respectively fixed to (forexample, detachably fixed to) the frame 101.

As illustrated in FIG. 2A and FIG. 2B, the baseplate 3 is configured tosupport the objects (the commodities) placed thereon. For example, inthe case where the goods cabinet is configured to support the objects(the commodities) in layers, the roof-plate 1 may also be served as thebaseplate, in another layer, of the goods cabinet.

As illustrated in FIG. 2A and FIG. 2B, the baseplate 3 may be dividedinto a plurality of recognition regions (or the commodity regions), andthe plurality of recognition regions are respectively configured tosupport the plurality of kinds of objects (commodities). For example,the plurality of recognition regions comprises a first recognitionregion 31, a second recognition region 32, a third recognition region33, and a fourth recognition region 34. For example, the plurality ofkinds of objects comprises a first object 51, a second object 52, athird object (not illustrated in figures), and a fourth object 54. Forexample, the plurality of kinds of objects have a plurality of differentheights (that is, lengths in the height direction), respectively.

As illustrated in FIG. 2A and FIG. 2B, the goods cabinet comprises alength direction (i.e., a first direction D1), a height direction (i.e.,a third direction D3) and a width direction (i.e., a second directionD2). For example, the first direction D1, the second direction D2 andthe third direction D3 are intersected with each other (for example, areperpendicular to each other).

As illustrated in FIG. 2A and FIG. 2B, the side-plate 6 is provided atat least one side (for example, two sides) of the plurality ofrecognition regions in the first direction D1; and the back plate 102 isprovided at at least one side (for example, one side) of the pluralityof recognition regions in the second direction D2.

In an example, as illustrated in FIG. 2A and FIG. 2B, the plurality ofrecognition regions 3 are arranged in parallel along the first directionD1; in this case, at least one reflective part 12 of the imageacquisition device comprises a first reflective part 121, and the firstreflective part 121 is provided at one side of the plurality of kinds ofobjects 5 in the first direction D1.

In another example, the plurality of recognition regions 3 are arrangedin parallel along the second direction D2; in this case, at least onereflective part 12 of the image acquisition device comprises a firstreflective part, and the first reflective part is provided at one sideof the plurality of kinds of objects 5 in the second direction D2.

In further another example, the plurality of recognition regions 3 arearranged in parallel along the first direction D1, and are furtherarranged in parallel along the second direction D2 intersecting thefirst direction D1 (i.e., the plurality of recognition regions 3 arearranged in an array); in this case, at least one reflective part 12 ofthe image acquisition device comprises a first reflective part and asecond reflective part 122 (not illustrated in figures), and the firstreflective part is at one side of the plurality of kinds of objects 5 inthe first direction D1, and the second reflective part 122 is at anotherside of the plurality of kinds of objects 5 in the second direction D2.

In an example, as illustrated in FIG. 2A and FIG. 2B, the camera of theimage acquisition device 4 is on the roof-plate 1 (for example, thesurface of the roof-plate 1 closer to the baseplate). In some examplesof the present disclosure, the camera is at the centerline, whichextends along the width direction of the goods shelf, of the roof-plate,that is, the camera may be provided on the centerline (for example, thecenter of the roof-plate), which extends along the second direction, ofthe roof-plate. As illustrated in FIG. 2A and FIG. 2B, in the case wherethe camera is provided on the centerline of the roof-plate, theacquisition angle, with respect to the objects on the goods shelf, ofthe camera is relatively large, and therefore, the difficulty insubsequent image recognition can be reduced.

In an example, as illustrated in FIG. 2A and FIG. 2B, at least onereflective part 12 of the image acquisition device is at the side-plate6 (for example, the surface of the side-plate 6 closer to therecognition regions 3); in this case, the reflective surface of thereflective part 12 faces toward the recognition regions 3, and theplurality of recognition regions are arranged in parallel along thefirst direction or arranged in an array.

In an example, at least one reflective part also may be provided at theback plate 102 (for example, the surface of the back plate 102 closer tothe recognition regions 3); in this case, the reflective surface of thereflective part 12 faces toward the recognition regions 3, and theplurality of recognition regions are arranged in parallel along thesecond direction or arranged in an array. For example, in the case wherethe baseplate is divided into the plurality of recognition regions alongthe width direction of the baseplate (the goods cabinet), the at leastone reflective part may be provided on the back plate.

It should be noted that, according to specific implementation, the goodscabinet also may not be provided with the back plate and the side-plate,in this case, the reflective part 12 may be provided at at least one ofthe positions where the back plate and the side-plate are located, asillustrated in FIG. 2A and FIG. 2B.

For example, as illustrated in FIG. 2A and FIG. 2B, the goods shelf mayfurther comprise a division plate 103 (for example, a plurality ofdivision plates 103), and each of the division plates 103 is provided ata spacing between adjacent recognition regions 103.

For example, as illustrated in FIG. 2A and FIG. 2B, the goods shelffurther comprises a front baffle stripe 8, and the front baffle stripe 8may be at a side of the roof-plate 1 (for example, the side of theroof-plate 1 closer to a moving passage for consumers). Labels 2 areplaced on the front baffle stripe, such that different objects can berecognized by a user. In the case where the objects are commodities forselling, the labels each may comprise information such as name, place ofproduction, and period of validity of the commodities for selling. Inthe case where the objects are samples for experiments, the labelscomprise information such as names, storage time, and origin of thesamples for experiment. For example, an edge of the roof-plate 1 may beprovided with the labels 2.

For example, the position of the camera and the position and the numberof the reflective part(s) may be set according to specificimplementations when acquiring the image of the objects on the goodsshelf with the image acquisition device provided by the embodiments ofthe present disclosure. Specifically, as illustrated in related figures,the camera may be on the roof-plate (e.g., attached to the roof-plate),and the reflective part may be on the side-plates at both sides of thegoods shelf. In order to recognize different objects with better effect,the baseplate is divided into the plurality of recognition regions, eachrecognition region is configured for holding the same kind of objects,and different recognition region are configured for holding differentkinds of objects. Even though FIG. 1 only illustrates the technicalsolution that the baseplate is divided into the plurality of recognitionregions arranged in parallel along the length direction (the firstdirection D1) of the baseplate (the goods cabinet), the technicalsolution that the baseplate is divided into the plurality of recognitionregions arranged in parallel along the width direction (the seconddirection D2) of the baseplate (the goods cabinet) may also be adopted.

For example, by introducing the reflective part, the blind zone problemsthat is presented in the image acquisition of the camera, especiallyedge type blind zone (for example, the blind zone at the edge of thegoods shelf) can be addressed.

At least one embodiment of the present disclosure provides a monitoringdevice for a goods shelf, which comprises: a processor and a memory.Computer program instructions that are suitable to be executed by theprocessor of a computer are stored in the memory; upon the processorrunning (executing) the computer program instructions, the monitoringdevice for the goods shelf performs a method comprising: obtaining adetermination result regarding whether or not an image of objects (imageof real objects) on the goods shelf comprises an occlusion area;obtaining the image of the objects and an image of a virtual image ofthe objects (virtual image of the real objects) and performing imagerecognition based on the image of the objects and an image region, whichis corresponding to the occlusion area, of the image of the virtualimage in a case where the determination result is that the image of theobjects comprises the occlusion area; and obtaining the image of theobjects and performing image recognition based on the image of theobjects in the case where the determination result is that the image ofthe objects does not comprise the occlusion area.

Non-limitative descriptions are given to the monitoring device for thegoods shelf provided by at least an embodiment of the present disclosurein the following with reference to a plurality of examples. As describedin the following, in case of no conflict, different features in thesespecific examples may be combined so as to obtain new examples, and thenew examples are also fall within the scope of present disclosure.

FIG. 7 illustrates an exemplary block diagram of a monitoring device fora goods shelf provided by at least an embodiment of the presentdisclosure. As illustrated in FIG. 7, the monitoring device for thegoods shelf comprises a processor and a memory. Computer programinstructions that are suitable to be executed by the processor arestored in the memory; upon the processor running the computer programinstructions, the monitoring device for the goods shelf performs thefollowing step S310 and step S320.

Step S310: obtaining a determination result regarding whether or not animage of objects on the goods shelf comprises an occlusion area.

Step S320: obtaining the image of the objects and an image of a virtualimage of the objects and performing image recognition based on the imageof the objects and an image region, which is corresponding to theocclusion area, of the image of the virtual image in a case where thedetermination result is that the image of the objects comprises theocclusion area; and obtaining the image of the objects and performingimage recognition based on the image of the objects in the case wherethe determination result is that the image of the objects does notcomprise the occlusion area.

In some examples, by performing image recognition based on the image ofthe objects and the image region, which is corresponding to theocclusion area, of the image of the virtual image in the case where thedetermination result is that the image of the objects comprises theocclusion area, information of the object in the occlusion area can beobtained, such that the accuracy of the result of the image recognitionand the monitoring effect of the monitoring device for the goods shelfcan be improved.

For example, upon the processor running the computer programinstructions, the monitoring device for the goods shelf performs thefollowing step S330.

Step S330: determining whether or not at least one of a group consistingof commodity shortage and commodity misplacement exists based on aresult of the image recognition; and uploading at least one of a groupconsisting of information related to the commodity shortage andinformation related to the commodity misplacement to a server in a casewhere it is determined that the at least one of the group consisting ofthe commodity shortage and the commodity misplacement exists.

For example, the commodity shortage means that the number of thecommodity or commodities of a specific kind on the goods shelf issmaller than the stock reminding threshold of the commodity of thespecific kind. For example, the stock reminding threshold may be setbased on the average replenishment time of the specific kind ofcommodity (i.e., average time from placing an order with a wholesaleruntil having the specific kind of commodity to be shipped to the goodsshelf), average on-shelf time of the specific kind of commodity (i.e.,the average value of the time between placing the commodity on the goodsshelf and successfully selling out the commodity), and quality guaranteeperiod of the specific kind of commodity. For example, in the case wherethe average replenishment time is relatively short, the average on-shelftime is relatively long, and the quality guarantee period is relativelyshort, the stock reminding threshold may be set to be a relatively smallvalue (for example, 1 or 2). For example, in the case where the averagereplenishment time is relatively long, the average on-shelf time isrelatively short, and the quality guarantee period is relatively long,the stock reminding threshold may be set to be a relatively large value(for example, 5). For example, because the number of the third commodity(i.e., third object) as illustrated in FIG. 2B is equal to zero, it maybe determined that the commodity shortage exists (shortage of the thirdcommodity exists).

For example, the commodity misplacement means that one of therecognition regions is placed with a commodity that does not belong tothis recognition regions. For example, in the case where the fourthcommodity (i.e., fourth object) or a commodity that does not belong tothe first commodity (i.e., first object) is within the first recognitionregion 101, it can be determined that the commodity misplacement exists(the commodity misplacement exists in the first recognition region 101).

For example, the server may be a general purpose server or a specialpurpose server and may be a virtual server, a cloud server, etc.

For example, at least one of a group consisting of information relatedto the commodity shortage and information related to the commoditymisplacement may be uploaded (sent) to a server through a communicationdevice.

For example, the communication device may send at least one of theinformation related to the commodity shortage and the informationrelated to the commodity misplacement to the server through a networktechnology or other technologies. For example, the network may be theInternet, wireless local area network (WLAN), mobile communicationnetwork and the like; for example, the other technologies can includeBluetooth communication technology, infrared communication technology,etc. For example, the communication device may comprise a modem, anetwork adapter, a Bluetooth transmitter and receiver, or an infraredtransmitter and receiver, etc. For example, the communication device mayalso perform operations such as coding and decoding of the sentinformation or the received information.

For example, performing of the image recognition based on the image ofthe objects and the image region, which is corresponding to theocclusion area, of the image of the virtual image comprises the step 321and the step 322.

Step 321: replacing the occlusion area with the image region, which iscorresponding to the occlusion area, of the image of the virtual image,so as to obtain a processed image; and

Step 321: performing the image recognition based on the processed image.

For example, before replacing the occlusion area with the image region,which is corresponding to the occlusion area, of the image of thevirtual image, a mirror symmetry operation may be performed with respectto the image region, which is corresponding to the occlusion area, ofthe image of the virtual image, and then the occlusion area in the imageof the objects is replaced with the image region processed with themirror symmetry operation, such that the difficulty of subsequent imagerecognition processes may be decreased.

For example, upon the processor running the computer programinstructions, the monitoring device for the goods shelf performs thefollowing step S340.

Step S340: obtaining the image of the objects and the image of thevirtual image based on a picture which is taken by a camera of an imageacquisition device of the goods shelf.

For example, the specific method regarding obtaining the image of theobjects and the image of the virtual image based on the picture which istaken by the camera of the image acquisition device of the goods shelfmay be set according to specific implementation, and no specificlimitation will be given in the embodiments of the present disclosure.

In an example, the picture which is taken by the camera may be dividedinto the image of the objects and the image of the virtual image throughusing the division plates of the goods shelf as a reference. Forexample, for the example as illustrated in FIG. 2A and FIG. 2B, thegoods shelf comprises three division plates, and the picture which istaken by the camera comprises six division plates (for example, arrangedin parallel along the first direction D1), the picture which is taken bythe camera is divided into the image of the objects and the image of thevirtual image along the spacing between the commodities between twodivision plates at the center of the six division plate.

In another example, the picture which is taken by the camera may bedivided according to a pre-stored dividing result of a reference image,and the dividing result of the reference image may be obtained throughthe following steps. Firstly, a reference image (for example, an initialimage) may be obtained through taking a picture of the goods shelf thatis not placed with the commodities by the camera; secondly, performingpre-dividing with respect to the reference image, so as to obtain anobject region and a virtual image region. For example, the pre-dividingmay be realized through manual dividing or automatic dividing using thedivision plates as references; and the dividing result of the referenceimage is stored.

For example, upon the processor running the computer programinstructions, the monitoring device for the goods shelf performs thefollowing step S350.

Step S350: dividing the image of the objects into a plurality of firstimage regions; and dividing the image of the virtual image into aplurality of second image regions.

For example, the plurality of first image regions are respectivelycorresponding to a plurality of recognition regions of a baseplate ofthe goods shelf; and the plurality of second image regions arerespectively corresponding to the plurality of recognition regions ofthe baseplate of the goods shelf. For example, the plurality of firstimage regions and the plurality of recognition regions have a one-to-onecorrespondence therebetween; and the plurality of second image regionsand the plurality of recognition regions have a one-to-onecorrespondence therebetween.

For example, dividing of the image of the objects into the plurality offirst image regions and dividing of the image of the virtual image intothe plurality of second image regions are in favor of replacing theocclusion area with the image region, which is corresponding to theocclusion area, of the image of the virtual image, and also is favorableto subsequent detection of at least one of the commodity misplacementand the commodity shortage.

For example, specific method of dividing the image of the objects intothe plurality of first image regions and dividing the image of thevirtual image into the plurality of second image regions may be setaccording to specific implementation, and no specific limitation will begiven in the embodiments of the present disclosure. For example, thedividing method based on a reference object (for example, using thedivision plate of the goods shelf as a reference) or the pre-dividingmethod (for example, the image is divided through using the dividingresult of the reference image) that are described in step S340 may beadopted.

For example, upon the processor running the computer programinstructions, the monitoring device for the goods shelf performs thefollowing step S360.

Step S360: obtaining the occlusion area of the image of the objects.

For example, specific method regarding obtaining the occlusion area ofthe image of the objects may be set according to specificimplementation, and no specific limitation will be given in theembodiments of the present disclosure.

In an example, the occlusion area may be obtained through comparing theimage of the objects and the image of the virtual image. For example,the object number reduction area (i.e., the area in which the number ofobjects is reduced) of the image of the objects is taken as theocclusion area; here, the number of the objects in the object numberreduction area is smaller than the number of the virtual objects in theimage region, which is corresponding to the object number reductionarea, of the image of the virtual image. For example, the number of theobjects in each of the first image region and the number of the virtualobjects in each of the second image region may be obtained, and then thenumbers of the objects (or virtual objects) in the first image regionand the second image region that are corresponding to same onerecognition region are compared to determine whether or not the firstimage region which is corresponding to the same recognition region is anobject number reduction area, and all the first image region which isdetermined as the object number reduction area may be taken as theocclusion area of the image of the objects.

In another example, the occlusion area of the image of the objects maybe obtained based on the lengths of the objects along the heightdirection of the goods shelf, the height of the camera of the imageacquisition device of the goods shelf (that is, the distance between thecamera and the base plate of the goods shelf) and the distance betweenadjacent objects in the direction along which the recognition regionsare arranged in parallel. For example, the occlusion area of the imageof the objects may be obtained based on calculation of similar triangleswith the above-mentioned parameters. For example, the method ofobtaining the occlusion area of the image of the objects based oncalculation of similar triangles may refer to the example as illustratedin FIG. 5, and no further descriptions will be given here.

In further another example, in the case where information that theheight of the commodities in the outmost recognition region of theplurality of recognition regions is relatively small can be known inadvance, the image region, which is corresponding to the outmostrecognition region of the plurality of recognition regions, of the imageof the objects may be directly taken as the occlusion area.

In further another example, the goods shelf may be provided with sensors(for example, weight sensors on the baseplate) that are configured todetect the number of the commodities (objects). For each of therecognition regions, in the case where the number, which is obtainedbased on the picture which is taken by the camera, of the commodities issmaller than the number, which is obtained by the sensors, of thecommodities, it can be determined that the image region, which iscorresponding to the recognition region, of the image of the objects isthe occlusion area.

For example, the monitoring device for the goods shelf may be providedon the goods shelf. For another example, the monitoring device for thegoods shelf may also be implemented as a server. For example, signalsand/or instructions may be transmitted between the monitoring device forthe goods shelf and the image acquisition device through a wired way ora wireless way. For example, wireless signal transmission may berealized through a network technology or other technologies, and thenetwork technology or the other technologies may refer to theabove-mentioned descriptions, and no further descriptions will be givenhere.

The processor, for example, is a central processing unit (CPU) or aprocessing unit in other forms having data processing capability and/orinstruction execution capability. For example, the processor may beimplemented as a general-purpose processor (GPP) and may also be amicrocontroller, a microprocessor, a digital signal processor (DSP), aspecial-purpose image processing chip, a field programmable logic array(FPLA), and the like. The memory, for example, may include a volatilememory and/or a non-volatile memory, for example, may include aread-only memory (ROM), a hard disk, a flash memory, and the like.Correspondingly, the memory may be implemented as one or more computerprogram products. The computer program products may include computerreadable storage media in various forms. One or more computer programinstructions may be stored in the computer readable storage medium. Theprocessor may run the program instructions to realize the function ofthe control device in the embodiment of the present disclosure asdescribed below and/or other desired functions. The memory may alsostore various other application programs and various data, for example,the determination result regarding whether or not an occlusion areaexists in the image of the objects on the goods shelf, the image of theobjects and the image of the virtual image.

For example, in the case where the step S340, the step S350 and the stepS360 are included, the step S340, the step S350 and the step S360 may beexecuted before executing of the step S310. For example, the step S330may be executed after the step S320 is executed.

At least one embodiment of the present disclosure provides a monitoringmethod for a goods shelf, which comprises: obtaining a determinationresult regarding whether or not an image of objects on the goods shelfcomprises an occlusion area; obtaining the image of the objects and animage of a virtual image of the objects and performing image recognitionbased on the image of the objects and an image region, which iscorresponding to the occlusion area, of the image of the virtual imagein a case where the determination result is that the image of theobjects comprises the occlusion area; and obtaining the image of theobjects and performing image recognition based on the image of theobjects in the case where the determination result is that the image ofthe objects does not comprise the occlusion area. For example, specificimplementations of the monitoring method for the goods shelf may referto the monitoring device for the goods shelf, and no furtherdescriptions will be given here.

For example, the monitoring device for the goods shelf and themonitoring method for the goods shelf can improve the accuracy indetecting at least one of the commodity misplacement and the commodityshortage through reducing the photograph blind zone.

FIG. 3 illustrates an exemplary flow chart of an image recognitionmethod based on a goods shelf, provided by at least an embodiment of thepresent disclosure. For example, the image recognition method based onthe goods shelf can increase the accuracy in detecting at least one ofthe commodity misplacement and the commodity shortage through reducingthe photograph blind zone. As illustrated in FIG. 3, the imagerecognition method based on the goods shelf comprises the following stepS101-step S103.

Step S101: obtaining an image (for example, the image is obtainedthrough taking a picture), which serves as an initial image (forexample, a reference image), of the baseplate of the goods shelf that isempty (that is, the goods shelf that is still not placed with anycommodities).

Step S102: obtaining an image, which serves as a recognition image andis obtained through taking a picture of the objects on the baseplate anda virtual image of the objects after the objects are placed on thebaseplate, and comparing the recognition image and the initial image, soas to obtain an image of the virtual image and an image of the objects(for example, an image of real image).

Step S103: determining whether or not a blind zone that is shelteredexists according to the image of the virtual image and the image of theobjects (for example, the image of real image), and replacing arecognition region, which is corresponding to the blind zone, of theimage of the objects with a recognition region, which is correspondingto the blind zone, of the image of the virtual image in a case where itis determined that the blind zone exists.

For example, the above-mentioned image recognition method based on thegoods shelf may be realized through the following steps. Firstly,obtaining (for example, the image is obtained through taking a picture)an image, which serves as an initial image, of the baseplate of thegoods shelf that is empty. Secondly, obtaining an image, which serves asa recognition image and is obtained through taking a picture of thebaseplate of the goods shelf that is placed with the objects (taking apicture of the objects on the baseplate and a virtual image of theobjects after the objects are placed on the baseplate); in this case,the recognition image comprises the virtual image, which is formed byreflection of the reflective part, of the object. Thirdly, comparing thefeatures of the recognition image and the initial image, such that animage of the virtual image can be recognized (such that an image of thevirtual image and an image of the objects can be obtained). Finally,determining whether or not a blind zone exists according to the image ofthe virtual image and the image of the objects (for example, the imageof real image), and replacing the recognition region corresponding tothe blind zone with the recognition region, which is corresponding tothe blind zone, of the image of the virtual image, so as to improve theaccuracy of recognizing the objects.

FIG. 4 illustrates an exemplary flow chart of the step S103 provided byat least an embodiment of the present disclosure. As illustrated in FIG.4, determination method regarding whether or not a blind zone existscomprises the following step S111-step S112.

Step S111: performing image recognition with respect to objects in theimage of the objects and virtual objects (virtual images of theobjects), which is corresponding to the objects in the image of theobjects, in the image of the virtual image, respectively; anddetermining that the blind zone does not exist in a case where theobjects in the image of the objects are the same as the virtual objects,which is corresponding to the objects in the image of the objects, inthe image of the virtual image.

Step S112: determining that the blind zone exists in a case where theobjects in the image of the objects are not same as the virtual objects,which is corresponding to the objects in the image of the objects, inthe image of the virtual image.

As illustrated in FIG. 1, the camera cannot take a picture of the secondobject (for example, the real image of the second object), but can takea picture of the first object and the second virtual image 2′ (thevirtual object of the second virtual image 2′) of the second object 2.In this case, because the object in the recognition region in which thesecond object is located is different from the virtual object in therecognition region in which the object of the second virtual image 2′ islocated, it is determined that the recognition region in which thesecond object is located is the blind zone. In the case where the objectin the image of the objects (for example, the image of real image) andthe corresponding virtual object in the image of the virtual image arethe same, it is determined that no blind zone exists. It should be notedthat, when determining whether or not the object in the image of theobjects (for example, the image of real image) and the virtual object inthe image of the virtual image are objects which are corresponding toeach other, the recognition region serves as a basis of judgment, thatis, after the recognition region, which is corresponding to therecognition region of the image of the objects (for example, the imageof real image), of the image of the virtual image are determined, theobject in the recognition region of the image of the objects (forexample, the image of real image) and the virtual object in therecognition region of the image of the virtual image are respectivelyrecognized.

FIG. 5 illustrates an exemplary schematic diagram of calculating of ablind zone provided by at least an embodiment of the present disclosure.Whether or not the photograph blind zone exists in the edge recognitionregion q2 close to the side-plate 6 can also be determined through thefollowing equation 1.

$\begin{matrix}{{h\; 2} = {{\frac{{d\; 1} - {d\; 2}}{d\; 1}H} + {\frac{d\; 2}{d\; 1}h\; 1}}} & {{equation}\mspace{14mu}(1)}\end{matrix}$

Here, H is the height of the camera (that is, the distance between thecamera and the baseplate); h1 is the height of the first object (thatis, the length of the first object in the height direction) in therecognition region q1; h2 is the height of first intersection point(that is, the distance between the intersection point and the baseplate)of a first virtual line and a second virtual line, in which the firstvirtual line connects the camera 11 and the top of the first object, andthe second virtual line passes through the second object and is parallelto the height direction (h1>h2); d1 is the distance, in the horizontaldirection, between the first object and the orthographic projection ofthe camera on the baseplate; d2 is the distance, in the horizontaldirection, between the second object and the orthographic projection ofthe camera on the baseplate; D is distance, in the horizontal direction,between the orthographic projection of the camera on the baseplate and asecond intersection point of the first virtual line and the baseplate.The above-mentioned equation (1) is obtained based on similar triangletheory.

In the case where the height of the second object is smaller than h2,the photograph blind zone 13 is presented and the second object in thephotograph blind zone 13 may not be recognized only based on the imageof objects on the goods shelf.

At least an embodiment of the present disclosure further providesanother image recognition method based on the goods shelf.

FIG. 6 illustrates another exemplary flow chart of the image recognitionmethod for the goods shelf provided by at least an embodiment of thepresent disclosure. As illustrated in related figures, the imagerecognition method comprises the following step S201-step S203.

Step S201: obtaining an image (for example, the image is obtainedthrough taking a picture), which serves as an initial image (forexample, a reference image), of the baseplate of the goods shelf that isempty (that is, the goods shelf that is not placed with thecommodities).

Step S202: obtaining an image, which serves as a recognition image andis obtained through taking a picture of the objects on the baseplate anda virtual image of the objects after the objects are placed on thebaseplate, and comparing the recognition image and the initial image, soas to obtain an image of the virtual image and an image of the objects(for example, an image of real image).

Step S203: replacing a recognition region, which is corresponding to ablind zone, of the image of the objects (for example, the image of realimage) with a recognition region, which is corresponding to the blindzone, of the image of the virtual image.

The difference between the image recognition method as illustrated inFIG. 6 and the image recognition method as illustrated in FIG. 3 is inthe last step. The step S203 does not comprise determining of whether ornot the blind zone exist, and the recognition region, which iscorresponding to the blind zone, of the image of the objects (forexample, the image of real image) is directly replaced with therecognition region, which is corresponding to the blind zone, of theimage of the virtual image. For example, in the case where therecognition accuracy and/or precision of the formed virtual image isrelatively high, replacing of the image of object (for example, theimage of real image) with the image of the virtual image does notadversely affect the recognition of the objects.

The flowcharts and block diagrams in the accompanying drawingsillustrate the possible architecture, functions and operations ofsystems, methods and computer program products provided by theembodiments of the present disclosure. In this regard, each block in aflowchart or block diagram may represent a module, program segment, orpart of a code, and the above-mentioned module, program segment, or partof the code comprises one or more executable instructions forimplementing a desired logical function. It should also be noted that,in some alternative implementations, the functions described by thecontents in the boxes may also be executed in a different order from theorder that is illustrated in the drawings. For example, two contiguousboxes may be executed in an essentially parallel mode in a specificimplementation, and may also be executed in a reverse order, dependingon the function involved. It should also be noted that each box in theblock diagram and/or flow chart, and the combination of the boxes in theblock diagram and/or flow chart may be implemented by a dedicatedhardware-based system that performs desired functions or operations, ormay be implemented by a combination of dedicated hardware and computerinstructions.

Although detailed description has been given above to the presentdisclosure with general description and embodiments, it shall beapparent to those skilled in the art that some modifications orimprovements may be made on the basis of the embodiments of the presentdisclosure. Therefore, all the modifications or improvements madewithout departing from the spirit of the present disclosure shall allfall within the scope of protection of the present disclosure.

What are described above is related to the illustrative embodiments ofthe disclosure only and not limitative to the scope of the disclosure;the scopes of the disclosure are defined by the accompanying claims.

What is claimed is:
 1. An image acquisition device, being within a goodsshelf comprising a goods cabinet, wherein the goods cabinet comprises abaseplate, the baseplate is divided into a plurality of recognitionregions, the plurality of recognition regions are respectivelyconfigured to support a plurality of kinds of objects, the imageacquisition device, comprising: at least one reflective part which is atleast one side of the plurality of kinds of objects in a direction alongwhich the plurality of recognition regions are arranged in parallel andconfigured to form a virtual image of an object through reflection ofthe object; and a camera which is at a side of the plurality of kinds ofobjects away from the baseplate and configured to take a picture of theobject and the virtual image of the object, so as to reduce a photographblind zone.
 2. The image acquisition device according to claim 1,wherein the reflective part is at a side of the object away from thecamera; and the reflective part is a plane mirror.
 3. The imageacquisition device according to claim 2, wherein the plane mirror isperpendicular to a plane where the object is placed; or the anglebetween the plane mirror and the plane where the object is placed isgreater than 90 degrees or smaller than 90 degrees.
 4. A goods shelf,comprising a goods cabinet and an image acquisition device, wherein theimage acquisition device comprises at least one reflective part and acamera; the at least one reflective part is configured to form a virtualimage of an object through reflection of the object; and the camera isconfigured to take a picture of the object and the virtual image of theobject, so as to reduce a photograph blind zone; the goods cabinetcomprises a baseplate; the baseplate is divided into a plurality ofrecognition regions, the plurality of recognition regions arerespectively configured to support a plurality of kinds of objects; thecamera of the image acquisition device is at a side of the plurality ofkinds of objects away from the baseplate; and the at least onereflective part of the image acquisition device is at at least one sideof the plurality of kinds of objects in a direction along which theplurality of recognition regions are arranged in parallel.
 5. The goodsshelf according to claim 4, wherein the plurality of recognition regionsare arranged in parallel along a first direction; the at least onereflective part of the image acquisition device comprises a firstreflective part, and the first reflective part is at one of the at leastone side of the plurality of kinds of objects in the first direction;the plurality of recognition regions is further arranged in parallelalong a second direction which intersects the first direction; and theat least one reflective part of the image acquisition device furthercomprises a second reflective part, and the second reflective part is atanother one of the at least one side of the plurality of kinds ofobjects in the second direction.
 6. The goods shelf according to claim4, wherein the goods cabinet further comprises a roof-plate and aside-plate; the side-plate is at at least one side of the plurality ofrecognition regions in the first direction; the plurality of recognitionregions is arranged in parallel along a first direction; the camera ofthe image acquisition device is on a surface of the roof-plate closer tothe baseplate; and the at least one reflective part of the imageacquisition device is on a surface of the side-plate closer to theplurality of recognition regions.
 7. The goods shelf according to claim6, wherein the camera is at a centerline, which extends along a seconddirection intersecting the first direction, of the roof-plate; the goodscabinet of the goods shelf further comprises a back plate; the backplate is at a side of the plurality of recognition regions in the seconddirection; the plurality of recognition regions is further arranged inparallel along the second direction; and the at least one reflectivepart is further on a surface of the back plate closer to the pluralityof recognition regions.
 8. A monitoring method for a goods shelf,comprising: obtaining a determination result regarding whether or not animage of objects on the goods shelf comprises an occlusion area;obtaining the image of the objects and an image of a virtual image ofthe objects and performing image recognition based on the image of theobjects and an image region, which is corresponding to the occlusionarea, of the image of the virtual image in a case where thedetermination result is that the image of the objects comprises theocclusion area; and obtaining the image of the objects and performingimage recognition based on the image of the objects in the case wherethe determination result is that the image of the objects does notcomprise the occlusion area.
 9. A monitoring device for a goods shelf,comprising: a processor and a memory, wherein computer programinstructions that are suitable to be executed by the processor arestored in the memory; and upon the processor running the computerprogram instructions, the monitoring device for the goods shelf performsthe monitoring method according to claim
 8. 10. The monitoring deviceaccording to claim 9, wherein upon the processor running the computerprogram instructions, the monitoring device for the goods shelf furtherperforms a following method comprising: determining whether or not atleast one of a group consisting of commodity shortage and commoditymisplacement exists based on a result of the image recognition; anduploading at least one of a group consisting of information related tothe commodity shortage and information related to the commoditymisplacement to a server in a case where it is determined that the atleast one of the group consisting of the commodity shortage and thecommodity misplacement exists.
 11. The monitoring device according toclaim 9, wherein performing of the image recognition based on the imageof the objects and the image region, which is corresponding to theocclusion area, of the image of the virtual image comprises: replacingthe occlusion area with the image region, which is corresponding to theocclusion area, of the image of the virtual image, so as to obtain aprocessed image; and performing the image recognition based on theprocessed image.
 12. The monitoring device according to claim 9, whereinupon the processor running the computer program instructions, themonitoring device for the goods shelf further performs a followingmethod comprising: obtaining the occlusion area of the image of theobjects.
 13. The monitoring device according to claim 9, wherein theocclusion area is obtained through comparing the image of the objectsand the image of the virtual image.
 14. The monitoring device accordingto claim 13, wherein the occlusion area is an object number reductionarea of the image of the objects; and a number of objects in the objectnumber reduction area is smaller than a number of virtual objects in animage region, which is corresponding to the object number reductionarea, of the image of the virtual image.
 15. The monitoring deviceaccording to claim 12, wherein the occlusion area of the image of theobjects is obtained based on lengths of the objects, a height of acamera of an image acquisition device of the goods shelf with respect toa plane where the objects are located, and a distance between adjacentobjects; or the occlusion area comprises a recognition region at veryoutside of the plurality of recognition regions.
 16. The monitoringdevice according to claim 9, wherein upon the processor running thecomputer program instructions, the monitoring device for the goods shelffurther performs a following method comprising: obtaining the image ofthe objects and the image of the virtual image based on a picture whichis taken by a camera of an image acquisition device of the goods shelf.17. The monitoring device according to claim 9, wherein upon theprocessor running the computer program instructions, the monitoringdevice for the goods shelf further performs a following methodcomprising: dividing the image of the objects into a plurality of firstimage regions; dividing the image of the virtual image into a pluralityof second image regions, wherein the plurality of first image regionsare respectively corresponding to a plurality of recognition regions ofa baseplate of the goods shelf; and the plurality of second imageregions are respectively corresponding to the plurality of recognitionregions of the baseplate of the goods shelf.
 18. An image recognitionmethod based on the goods shelf according to claim 4, comprising:obtaining an image, which serves as an initial image, of the baseplateof the goods shelf that is empty; obtaining an image which serves as arecognition image and is obtained through taking a picture of theobjects on the baseplate and a virtual image of the objects after theobjects are placed on the baseplate, and comparing the recognition imageand the initial image, so as to obtain an image of the virtual image andan image of the objects; and determining whether or not a blind zoneexists according to the image of the virtual image and the image of theobjects, and replacing a recognition region, which is corresponding tothe blind zone, of the image of the objects with a recognition region,which is corresponding to the blind zone, of the image of the virtualimage in a case where it is determined that the blind zone exists. 19.The image recognition method according to claim 18, wherein determiningof whether or not a blind zone exists comprises: performing imagerecognition with respect to objects in the image of the objects andvirtual objects, which is corresponding to the objects in the image ofthe objects, in the image of the virtual image, respectively;determining that the blind zone does not exist in a case where theobjects in the image of the objects are same as the virtual objects,which is corresponding to the objects in the image of the objects, inthe image of the virtual image; and determining that the blind zoneexists in a case where the objects in the image of the objects are notsame as the virtual objects, which is corresponding to the objects inthe image of the objects, in the image of the virtual image.
 20. Animage recognition method based on the goods shelf according to claim 4,comprising: obtaining an image, which serves as an initial image, of thebaseplate of the goods shelf that is empty; obtaining an image, whichserves as a recognition image and is obtained through taking a pictureof the objects on the baseplate and a virtual image of the objects afterthe objects are placed on the baseplate, and comparing the recognitionimage and the initial image, so as to obtain an image of the virtualimage and an image of the objects; and replacing a recognition region,which is corresponding to a blind zone, of the image of the objects witha recognition region, which is corresponding to the blind zone, of theimage of the virtual image.